A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance

Lee, Giyoung; Mallipeddi, Rammohan; Jang, Gil-Jin

doi:10.1109/lsp.2015.2417592

Cited by 47 publications

(15 citation statements)

References 10 publications

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“…The latter algorithm has been highlighted in providing efficiency as it covers a small area where the pixel changing occurs, hence it could save both memory and time such as those that applied the algorithm for traffic surveillance [10], [11] and examined image processing for detecting defects in industrial pipes [9].…”

Section: Motion Detection Algorithm and Alert Systemmentioning

confidence: 99%

A CCTV system with SMS alert (CMDSA): An implementation of pixel processing algorithm for motion detection

Rahman¹,

Abdullah²,

Hamid³

et al. 2017

AIP Conference Proceedings

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Section: Motion Detection Algorithm and Alert Systemmentioning

confidence: 99%

A CCTV system with SMS alert (CMDSA): An implementation of pixel processing algorithm for motion detection

Rahman¹,

Abdullah²,

Hamid³

et al. 2017

AIP Conference Proceedings

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“…Kang and Zhu [15] have adopted compressive sensing mechanism where circulate sampling mechanism is used for obtaining samples followed by a typical reconstruction method of the foreground. Usage of search optimization method is a witness in work carried out by Lee et al [16]. The authors have constructed saliency map using a genetic algorithm to track the movement of an object.…”

Section: Related Techniquesmentioning

confidence: 99%

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Pushpa¹

2017

ijacsa

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Abstract-The advancement of visual sensing has introduced better capturing of the discrete information from a complex, crowded scene for assisting in the analysis. However, after reviewing existing system, we find that majority of the work carried out till date is associated with significant problems in modeling event detection as well as reviewing abnormality of the given scene. Therefore, the proposed system introduces a model that is capable of identifying the degree of abnormality for an event captured on the crowded scene using unsupervised training methodology. The proposed system contributes to developing a novel region-wise repository to extract the contextual information about the discrete-event for a given scene. The study outcome shows highly improved the balance between the computational time and overall accuracy as compared to the majority of the standard research work emphasizing on event detection.

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“…In general, the UAV with a camera attached for surveillance flying over the mission area can be controlled manually by an operator or automatically by using computer vision. One of the most important tasks of aerial surveillance is the detection of moving objects that can be used to convey essential information in images, such as pedestrian detection and tracking [1][2][3], vehicle detection and tracking [4,5], object counting [6], estimation and recognition of object activity [7][8][9], human and vehicle interactions [10], intelligent transportation systems [11,12], traffic management [13,14], and autonomous robot navigation [15,16].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

2019

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Detection of moving objects by unmanned aerial vehicles (UAVs) is an important application in the aerial transportation system. However, there are many problems to be handled such as high-frequency jitter from UAVs, small size objects, low-quality images, computation time reduction, and detection correctness. This paper considers the problem of the detection and recognition of moving objects in a sequence of images captured from a UAV. A new and efficient technique is proposed to achieve the above objective in real time and in real environment. First, the feature points between two successive frames are found for estimating the camera movement to stabilize sequence of images. Then, region of interest (ROI) of the objects are detected as the moving object candidate (foreground). Furthermore, static and dynamic objects are classified based on the most motion vectors that occur in the foreground and background. Based on the experiment results, the proposed method achieves a precision rate of 94% and the computation time of 47.08 frames per second (fps). In comparison to other methods, the performance of the proposed method surpasses those of existing methods.

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A Genetic Algorithm-Based Moving Object Detection for Real-time Traffic Surveillance

Cited by 47 publications

References 10 publications

A CCTV system with SMS alert (CMDSA): An implementation of pixel processing algorithm for motion detection

A CCTV system with SMS alert (CMDSA): An implementation of pixel processing algorithm for motion detection

A Novel Unsupervised Abnormal Event Identification Mechanism for Analysis of Crowded Scene

Real-Time Detection and Recognition of Multiple Moving Objects for Aerial Surveillance

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